In the field of software engineering， code clone detection methods based on semantic similarity can reduce the cost of software maintenance and prevent system vulnerabilities. As a typical form of code abstract representation， Abstract Syntax Tree （AST） has achieved success in code clone detection tasks of many program languages. However， the existing work mainly uses the original AST to extract code semantics， and does not dig deep semantic and structural information in AST. To solve the above problem， a code clone detection method based on Dependency Enhanced Hierarchical Abstract Syntax Tree （DEHAST） was proposed. Firstly， the AST was layered and divided into different semantic levels. Secondly， corresponding dependency enhancement edges were added to different levels of AST to construct DEHAST， thus a simple AST was transformed into a heterogeneous graph with richer program semantics. Finally， a Graph Matching Network （GMN） model was used to detect the similarity of heterogeneous graphs to achieve code clone detection. Experimental results on two datasets BigCloneBench and Google Code Jam show that DEHAST is able to detect 100% of Type-1 and Type-2 code clones， 99% of Type-3 code clones， and 97% of Type-4 code clones； compared with the tree based method ASTNN （AST-based Neural Network）， the F1 values all increase by 4 percentage points. Therefore， DEHAST can effectively perform code semantic clone detection.

Aiming at the hidden danger of fire caused by electric bicycles and gas tanks taken into elevators， an improved attention mechanism was proposed to detect dangerous goods in elevator scene， and a method based on the mechanism was proposed. With YOLOX-s as the baseline model， firstly， a depthwise separable convolution was introduced in the enhanced feature extraction network to replace the standard convolution， which improved the reasoning speed of the model. Secondly， an Efficient Convolutional Block Attention Module （ECBAM） based on mixed-domain was proposed and embedded into the backbone feature extraction network. In the channel attention part of ECBAM， two fully connected layers were replaced by a one-dimensional convolution， which not only reduced the complexity of Convolutional Block Attention Module （CBAM） but also improved the detection precision. Finally， a multi-frame collaboration algorithm was proposed to reduce the false alarms of dangerous goods’ intrusion into the elevator by combining the dangerous goods detection results of multiple images. Experimental results show that compared with YOLOX-s， the improved model can increase the mean Average Precision （mAP） by 1.05 percentage points， reduce the floating point computational cost by 34.1% and reduce the model size by 42.8%. The improved model reduces false alarms in practical applications and meets the precision and speed requirements of dangerous goods detection in elevator scene.

To address the problems of Web crawler code failure and high manual maintenance cost caused by webpage source code changes led by frequent webpage redesigns， especially changes in element structures or attribute identifiers of target entities such as article dates， main body of text or source organizations， a self-adaptive Web crawler code generation method based on webpage source code structure comprehension was proposed. Firstly， the corresponding Web crawler code was extracted by analyzing the change patterns of webpage structural characteristics. Secondly， the changes in the webpage source code and code were represented by the Encoder-Decoder model. By fusing the semantic features of the webpage source code structure， the features of webpage source code changes and the features of webpage code changes， an adaptive code generation model was obtained. Finally， the perception， generation and activation mechanisms of the adaptive system were improved to form a Web crawler system with adaptive processing capability. Compared with TF-IDF+Seq2Seq and TriDNR+Seq2Seq models， the proposed adaptive code generation model was experimentally verified to show the superiority in the representation of webpage source code changes and the effectiveness of code generation with a final accuracy of 78.5%. With the proposed method， the Web crawler code operation problems caused by the webpage source code changes could be solved， and a new idea for the adaptive processing capability of Web resource acquisition — Web crawler technique was provided.

The algorithm platform， as the implementation way of automatic machine learning， has attracted the wide attention in recent years. However， the business processes of these platforms need to be built manually， and these platforms are faced with inflexible model calling and the incapability of customized automatic algorithm construction for specific business requirements. To address these problems， an algorithm path self-assembling model for business requirements was proposed. Firstly， the sequence features and structural features of code were modeled simultaneously based on Graph Convolutional Network （GCN） and word2vec representation. Secondly， functions in the algorithm set were further discovered through a clustering model， and the obtained function subsets were used for the preparation of the path discovery of algorithm components between subsets. Finally， based on the relationship discovery model and ranking model trained with prior knowledge， the self-assembled paths of candidate code components were mined， thus realizing the algorithm code self-assembling. Using the proposed evaluation indicators for comparison and analysis， the best result of the proposed algorithm path self-assembling model is 0.8， while that of the baseline model Okapi BM25+word2vec is 0.21. To a certain extent， the proposed model solves the problem of missing code structure and semantic information in traditional code representation methods and lays the foundation for the research of refinement of algorithm process self-assembling and automatic construction of algorithm pipelines.

To solve the problem of stockout， increasing inventory level and the fluctuation of order quantity caused by stochastic disturbance， an optimization model of inventory system under stochastic disturbance based on Active Disturbance Rejection Control （ADRC） was proposed. Firstly， according to the operational management logic behind the purchase-sale-storage product and information flows， the transfer function of the inventory system was obtained and transformed to a second-order state space standard form by the Laplace transform. Secondly， an optimization model of inventory system under stochastic disturbance based on ADRC including the tracking differentiator， the extended state observer and the nonlinear state error feedback control law was designed to control and compensate the adverse effects on the inventory system caused by stochastic disturbance under the premise of ensuing system stability. Finally， simulations were carried out by using data collected from the industry to verify the effectiveness of the optimization model on optimization of the inventory system under stochastic disturbance. Simulation results show that compared to the inventory feedback control model without ADRC， the optimization model of inventory system under stochastic disturbance based on ADRC has the residual inventory reduced by 40%， the average order quantity reduced by 47.4%， the order fluctuation decreased by 39.3%， and the stockout of enterprise inventory system caused by stochastic disturbance greatly improved. It can be seen that the optimization model of inventory system under stochastic disturbance based on ADRC can guide enterprises to make a reasonable ordering decision， decrease the inventory level， improve the stability of inventory system dynamically， and provide the scientific theoretical reference and countermeasures for the actual operations of enterprises.

In the process of network traffic classification, the traditional model has poor classification on minority classes and cannot be updated frequently and timely. In order to solve the problems, a network Traffic Classification Model based on Ensemble Learning (ELTCM) was proposed. First, in order to reduce the impact of class imbalance problem, feature metrics biased towards minority classes were defined according to the class distribution information, and the weighted symmetric uncertainty and Approximate Markov Blanket (AMB) were used to reduce the dimensionality of network traffic features. Then, early concept drift detection was introduced to enhance the model's ability to cope with the changes in traffic features as the network changed. At the same time, incremental learning was used to improve the flexibility of model update training. Experimental results on real traffic datasets show that compared with the Internet Traffic Classification based on C4.5 Decision Tree (DTITC) and Classification Model for Concept Drift Detection based on ErrorRate (ERCDD), the proposed ELTCM has the average overall accuracy increased by 1.13% and 0.26% respectively, and the classification performance of minority classes all higher than those of the models. ELTCM has high generalization ability, and can effectively improve the classification performance of minority classes without sacrificing the overall classification accuracy.

OpenID Connect protocol is widely used in identity authentication field and is one of the newest single sign-on protocols. In this paper, the digital signature and asymmetric encryption were used to improve OpenID connect protocol. The secrecy and authentication of the improved protocol were focused. And then the improved OpenID connect protocol was formalized with the applied PI calculus in the symbolic model, next the secrecy was modeled by query and the authentication was modeled by non-injective relations to test the secrecy and authentication of improved OpenID Connect protocol. Finally the formal model of the OpenID Connect protocol was transformed into the input of the automatic tool ProVerif based on symbol model. The results indicate that the improved OpenID Connect protocol is authenticable and secret.

Smart Wireless Water Meter Reading System (SWWMRS) built on the conventional Wireless Sensor Network (WSN) platform can not meet the requirements of low cost, low power consumption, high efficiency and high reliability in practice. In this work, a novel SWWMRS for typical multi-story buildings was proposed. Based on the feature of the SWWMRS and deployment environment as well as the business logic, an improved algorithm for all neighbor discoveries was proposed to achieve automatic networking and centralized routing management. At the meter reading stage, a minimum global forward strategy with a minimum residual energy nodes avoidance strategy were adopted to balance the energy consumption between nodes. Additionally, the mechanism to avoid confliction in Media Access Control (MAC) layer and the low power idle listening strategy were optimized. The testing results for the proposed system in a 24-story residential building show that the system performance of communication distance, power consumption and reliability can meet the needs of the practical applications. Meanwhile, compared with CC2530 scheme, better performance in communication distance, meter reading success rate, efficiency and power consumption can be achieved.

In view of the problems of traditional prepaid meters such as inefficiency and inconvenience, a Near Field Communication (NFC)-enabled water meter system that has the functions of mobile payment and data query was proposed. Firstly, according to the business requirements of the prepaid water meter, the overall architecture of the water meter system was developed based on NFC technology, and the software and hardware were designed. Secondly, a low-power mechanism which was used to wake up the water meter by detecting the external magnetic field changes was proposed. Finally, the security performance in mobile payment of the water meter system was analyzed based on NFC security protocols. The experimental results show that users can dynamically awake the water meter system, and utilize the functions of mobile payment, data querying and data uploading, by using the NFC mobile phones or other mobile terminals with NFC module.

Currently, most resource reallocation methods in cloud computing mainly aim to how to reduce active physical nodes for green computing, however, node stability of virtual machine placement solution is not considered. According to varying workload information of applications, a new virtual machine placement method based on multi-objective optimization was proposed for node stability, considering both the overhead of virtual machine reallocation and the stability of new virtual machine placement, and a new Multi-Objective optimization based Genetic Algorithm for Node Stability (MOGANS) was designed to solve this problem. The simulation results show that, the stability time of Virtual Machine (VM) placement obtained by MOGANS is 10.42 times as long as that of VM placement got by GA-NN (Genetic Algorithm for greeN computing and Numbers of migration). Meanwhile, MOGANS can well balance stability time and migration overhead.

Since the traditional wavelet threshold functions have some drawbacks such as the non-continuity on the points of threshold, and large deviation of estimated wavelet coefficient, distortion and Gibbs phenomenon occur after denoising. To overcome these drawbacks, an improved threshold function was proposed. Compared with the hard, soft threshold functions and the existing improved threshold function, the proposed function not only is easy to be calculated, but also has the superior mathematical characteristics.To verify its advantages, a series of simulation experiments were performed, the Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) values were compared with other different denoising methods.The experimental results indicate that it is better than above mentioned denoising methods in both the visual effects and the performance of PSNR and MSE.

Based on cloud computings architecture and the actual demands of Tsinghua University, followed by utilizing the advanced OpenStack platform, adopting hierarchical design method, the TsinghuaCloud platform that could be used to perform integrated management on cloud resources was designed and implemented. The advantages and main required module functions of this system were analyzed. Focusing on the resource scheduling, a strategy based on task scheduling and load balancing was proposed. The experiment and analysis of the scheduling plan verify that the scheduling strategy can balance servers resource load on the basis of ensuring its service performance and execution efficiency, so as to make the cloud platform relatively stable.

Because the functions of backup and recovery are usually ignored in the web anti-tamper system, a web anti-tamper system model is proposed in this paper, based on which a safe and efficient remote backup and recovery system is given. The multi-version management technical of backup data is used for restoring the data of different versions and different periods. The Lempel-Ziv-Markov chain Algorithm(LZMA) is used to compress the backup data for improving the disk utilization. The Data Encryption Standard(DES) and File Transfer Protocol(FTP) are used for securable remote date storage and transmission. Finally, the system performance test shows it can effectively backup and recover the WEB Server data without affecting the WEB Server load and recover a single page within 100ms. It’s an effective means to solve the web tampering problem.

To address the uncertainty of weights selection in Multi-source medical image fusion process,the basic probability assignment function of the evidence is used to express decision result’s uncertainty based on Dempster-Shafer (DS) evidential theory.The detection image’s three features,which are regional variance, regional energy, regional information entropy,are used and normalized,then the basic probability assignment can be got according to the features.Image fusion rules with multi-feature based on DS evidence theory is used for high frequency components in wavelet domain. Energy of Laplace adaptive fusion rules is used for low frequency component in wavelet domain according to energy of Laplace. Experiments show that the proposed algorithm is superior to other fusion algorithms.It combines the advantages of multi-feature,reduces the uncertainty during the image fusion process and retains the details of the image in large extent.

In this paper, a new real-time image transmission scheme based on Set Partitioning In Hierarchical Tree (SPIHT) was proposed. Firstly, the image data needed to be transformed by wavelet. Secondly, in order to resist error pervasion when image was transmitted, the wavelet coefficients were separated into small blocks and encoded by SPIHT. Finally, in order to improve the quality of the restructured image, the wavelet coefficients of the highest level in every block were transmitted repeatedly. In order to improve the throughput of the image transmission system, the optimum frame length was proposed. Both theoretical demonstration and simulation results here have validated that the proposed scheme provides stronger error resilience than traditional scheme based on SPIHT, and can improve the peak signal to noise ratio of the restructured image about 10dB.

The accuracy of sensor data is a critical index to evaluate the performance of Wireless Sensor Network (WSN). Outlier detection is a crucial but challenging issue for WSN. In this paper, an outlier detection approach based on variable-width histogram was proposed. The dynamic sensor data were aggregated into variable-width histograms, which avoided unnecessary data transmissions while detecting outliers. The theoretical analysis and evaluation on real WSN dataset show that this approach has high detection accuracy, and the cost is effectively reduced.

Focusing on WAP technology and its application in mobile Internet, which is popular at present, there was a research about using mature MVC pattern on designing such applications. It gave a brief introduction of WAP, WAP network and MVC pattern, and also introduced MVC pattern to WAP system, which made WAP program optimized. A concrete application was given as an example lastly.